Beta diversity
set.seed(1)
prob_table <- obj$data$prop[metadata$sample_name]
nmds_plot_data <- function(prob_table) {
metadata <- metadata[metadata$sample_name %in% colnames(prob_table), ]
set.seed(1)
nmds_results <- vegan::metaMDS(t(prob_table), trymax = 1000, k = 2, trace = 0)
nmds_data <- nmds_results$points %>%
as_tibble() %>%
bind_cols(metadata)
names(nmds_data)[1:2] <- paste0("NMDS", 1:2)
return(nmds_data)
}
nmds_data <- nmds_plot_data(prob_table[! is.na(metadata$organism) & metadata$valid_inoc])
nmds_factors <- c(Flooded = 'flooded', Organism = 'organism', 'Pathogen CFU/g' = 'path_conc', 'Trial' = 'experiment')
make_one_plot <- function(factor, name) {
nmds_data %>%
mutate(factor = as.character(nmds_data[[factor]]),
NMDS1 = scales::rescale(NMDS1),
NMDS2 = scales::rescale(NMDS2)) %>%
mutate(factor = fct_relevel(ordered(factor), "Control", "Cin", "Plu", "Cry")) %>%
ggplot(aes_string(x = "NMDS1", y = "NMDS2", color = "factor", label = "sample_name")) +
# geom_label(size = 2) +
geom_point() +
coord_fixed() +
viridis::scale_color_viridis(discrete = TRUE, end = .9) +
labs(color = NULL, x = NULL, y = NULL) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(),
axis.title = element_text(size = 7),
axis.ticks = element_blank(),
plot.margin = unit(rep(0.04, 4), "cm"),
# panel.background = element_rect(fill = 'transparent', colour = NA),
# plot.background = element_rect(fill = "white", colour = NA),
legend.position = "bottom",
legend.text = element_text(size = 10),
legend.key.height = unit(0.5, 'cm'),
legend.key.width = unit(0.2, 'cm'))
}
nmds_subplots <- map2(nmds_factors, names(nmds_factors), make_one_plot)
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `factor = fct_relevel(ordered(factor), "Control", "Cin", "Plu",
#> "Cry")`.
#> Caused by warning:
#> ! 4 unknown levels in `f`: Control, Cin, Plu, and Cry
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `factor = fct_relevel(ordered(factor), "Control", "Cin", "Plu",
#> "Cry")`.
#> Caused by warning:
#> ! 4 unknown levels in `f`: Control, Cin, Plu, and Cry
#> There was 1 warning in `mutate()`.
#> ℹ In argument: `factor = fct_relevel(ordered(factor), "Control", "Cin", "Plu",
#> "Cry")`.
#> Caused by warning:
#> ! 4 unknown levels in `f`: Control, Cin, Plu, and Cry
nmds_plot <- ggpubr::ggarrange(plotlist = c(list(ggplot() + theme_void()), nmds_subplots),
nrow = 1, widths = c(0.15, 1, 1, 1, 1))
ggsave(nmds_plot, path = "standard_workflow/combined_figures", filename = "nmds.pdf",
width = 7, height = 8)
print(nmds_plot)
combined_div_plot <- ggpubr::ggarrange(alpha_subplot, nmds_plot, ncol = 1, labels = c('A', 'B'),
heights = c(1, 1))
combined_div_plot
ggsave(combined_div_plot, path = "standard_workflow/combined_figures", filename = "diversity.pdf",
width = 10, height = 6, bg = "#FFFFFF")
ggsave(combined_div_plot, path = "standard_workflow/combined_figures", filename = "diversity.svg",
width = 10, height = 6, bg = "#FFFFFF")Let’s do associated PERMANOVA analyses to better understand significance of differences in diversity between the 3 factors
dist_matrix <- vegdist(t(prob_table), method = "bray")
adonis2_full <- adonis2(dist_matrix ~ organism + flooded + path_conc + experiment,
data = metadata,
permutations = 999,
method = "bray")
print(adonis2_full)
#> Permutation test for adonis under reduced model
#> Terms added sequentially (first to last)
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = dist_matrix ~ organism + flooded + path_conc + experiment, data = metadata, permutations = 999, method = "bray")
#> Df SumOfSqs R2 F Pr(>F)
#> organism 3 1.252 0.02381 1.3885 0.061 .
#> flooded 1 1.848 0.03514 6.1478 0.001 ***
#> path_conc 1 0.276 0.00524 0.9170 0.500
#> experiment 1 2.616 0.04974 8.7009 0.001 ***
#> Residual 155 46.599 0.88606
#> Total 161 52.592 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
adonis2_org <- adonis2(dist_matrix ~ organism,
data = metadata,
permutations = 999,
method = "bray")
print(adonis2_org)
#> Permutation test for adonis under reduced model
#> Terms added sequentially (first to last)
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = dist_matrix ~ organism, data = metadata, permutations = 999, method = "bray")
#> Df SumOfSqs R2 F Pr(>F)
#> organism 3 1.252 0.02381 1.2847 0.114
#> Residual 158 51.339 0.97619
#> Total 161 52.592 1.00000
adonis2_flooded <- adonis2(dist_matrix ~ flooded,
data = metadata,
permutations = 999,
method = "bray")
print(adonis2_flooded)
#> Permutation test for adonis under reduced model
#> Terms added sequentially (first to last)
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = dist_matrix ~ flooded, data = metadata, permutations = 999, method = "bray")
#> Df SumOfSqs R2 F Pr(>F)
#> flooded 1 1.849 0.03516 5.8301 0.001 ***
#> Residual 160 50.743 0.96484
#> Total 161 52.592 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
adonis2_path_conc <- adonis2(dist_matrix ~ path_conc,
data = metadata,
permutations = 999,
method = "bray")
print(adonis2_path_conc)
#> Permutation test for adonis under reduced model
#> Terms added sequentially (first to last)
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = dist_matrix ~ path_conc, data = metadata, permutations = 999, method = "bray")
#> Df SumOfSqs R2 F Pr(>F)
#> path_conc 1 0.308 0.00586 0.9434 0.487
#> Residual 160 52.283 0.99414
#> Total 161 52.592 1.00000
adonis2_experiment <- adonis2(dist_matrix ~ experiment,
data = metadata,
permutations = 999,
method = "bray")
print(adonis2_experiment)
#> Permutation test for adonis under reduced model
#> Terms added sequentially (first to last)
#> Permutation: free
#> Number of permutations: 999
#>
#> adonis2(formula = dist_matrix ~ experiment, data = metadata, permutations = 999, method = "bray")
#> Df SumOfSqs R2 F Pr(>F)
#> experiment 1 2.615 0.04972 8.3709 0.001 ***
#> Residual 160 49.977 0.95028
#> Total 161 52.592 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Let’s make new taxmap object in preparation to make heattree examining abundance and diversity of taxa in at least 5 samples
#Make sure our factors are input properly as factors
#Make sure our factors are input properly as factors
metadata$path_conc <- as.factor(metadata$path_conc)
metadata$path_conc<- factor(metadata$path_conc, levels = c("0", "1", "100"))
metadata$organism <- as.factor(metadata$organism)
metadata$organism<- factor(metadata$organism, levels = c("Control", "Cin", "Cry", "Plu"))
metadata$flooded <- as.factor(metadata$flooded)
metadata$flooded <- factor(metadata$flooded, levels = c("TRUE", "FALSE"))
metadata$experiment <- as.factor(metadata$experiment)
metadata$experiment<- factor(metadata$experiment, levels = c("1", "2"))
abundance$is_low_abund <- rowSums(abundance[, metadata$sample_name]) < 5
obj2 <- parse_tax_data(abundance, class_cols = 'dada2_tax', class_sep = ';',
class_regex = '^(.+)--(.+)--(.+)$',
class_key = c(taxon = 'taxon_name', boot = 'info', rank = 'taxon_rank'))
names(obj2$data) <- c('abund', 'score')
obj2 <- transmute_obs(obj2, 'score', sequence = sequence[input_index], boot = boot, rank = rank)
print(obj2)
#> <Taxmap>
#> 1572 taxa: aab. Eukaryota, aac. Fungi ... cim. NA
#> 1572 edges: NA->aab, aab->aac, aab->aad ... cik->cil, cil->cim
#> 2 data sets:
#> abund:
#> # A tibble: 2,697 × 179
#> taxon_id sequence dada2_tax is_inoculum inoculum S1 S10
#> <chr> <chr> <chr> <lgl> <chr> <int> <int>
#> 1 bfz AAAAAGTCG… Eukaryot… FALSE <NA> 0 0
#> 2 bga AAAAAGTCG… Eukaryot… FALSE <NA> 0 0
#> 3 bgb AAAAAGTCG… Eukaryot… FALSE <NA> 0 0
#> # ℹ 2,694 more rows
#> # ℹ 172 more variables: S100 <int>, S101 <int>, S102 <int>,
#> # S103 <int>, S104 <int>, S105 <int>, S106 <int>, S107 <int>,
#> # S108 <int>, S109 <int>, …
#> score:
#> # A tibble: 23,356 × 4
#> taxon_id sequence boot rank
#> <chr> <chr> <chr> <chr>
#> 1 aab AAAAAGTCGTAACAAGGTAACCGTAGGTGAACCTGCGGTT… 100 Doma…
#> 2 aac AAAAAGTCGTAACAAGGTAACCGTAGGTGAACCTGCGGTT… 100 King…
#> 3 aae AAAAAGTCGTAACAAGGTAACCGTAGGTGAACCTGCGGTT… 100 Phyl…
#> # ℹ 23,353 more rows
#> 0 functions:
obj2$data$tax_abund <- calc_taxon_abund(obj2, data = "abund", cols = metadata$sample_name)
#> Summing per-taxon counts from 162 columns for 1572 taxa
obj2$data$abund_prop <- calc_obs_props(obj2, data = "abund", cols = metadata$sample_name, groups = rep("tax_prop", nrow(metadata)))
#> Calculating proportions from counts for 162 columns in 1 groups for 2697 observations.
obj2$data$tax_prop <- calc_taxon_abund(obj2, data = "abund_prop", cols = "tax_prop")
#> Summing per-taxon counts from 1 columns for 1572 taxa
obj2$data$abund_prop <- NULL
obj_subset <- obj2 %>%
filter_taxa(taxon_ranks == "Species", supertaxa = TRUE, reassign_obs = c(tax_abund = FALSE))
min_bootstrap <- 0
obj_subset$data$score$boot <- as.numeric(obj_subset$data$score$boot)
max_boot <- obj_subset$data$score %>%
group_by(taxon_id) %>%
summarise(max = max(boot))
max_boot <- setNames(max_boot$max, max_boot$taxon_id)
obj_subset <- filter_taxa(obj_subset, max_boot[taxon_ids] >= min_bootstrap | taxon_ranks %in% c("ASV", "Reference"), reassign_obs = c(abund = TRUE, score = FALSE))Let’s make differential abundance plots for our combined dataset-first let’s look at diff. abundance of taxa by inoculum concentration
#> [1] 1.9483e-11 1.0000e+00
#> [1] -9.825868 29.147210
Let’s make differential abundance plots for our combined dataset-first let’s look at diff. abundance of taxa by whether plots were flooded or not
#> [1] 3.732973e-21 9.956213e-01
#> [1] -24.79712 5.11938
Let’s make differential abundance plots for our combined dataset-first let’s look at diff. abundance of taxa by inoculum source
#> [1] 2.67702e-46 1.00000e+00
#> [1] -42.21227 41.73490
Let’s make differential abundance plots for our combined dataset-first let’s look at diff. abundance of taxa by whether pots were in trial 1 or 2
#> [1] 1.147702e-192 9.859411e-01
#> [1] -29.43794 25.78321
Let’s try to facet these plots.
#> quartz_off_screen
#> 2Let’s finally make an exploratory heat tree to look at community composition across all samples, just retaining ASVs present at least 10 times
Overall community plot
obj_subset <- obj2 %>%
filter_taxa(taxon_ranks == "Species", supertaxa = TRUE, reassign_obs = c(tax_abund = FALSE))
min_bootstrap <- 60
obj_subset$data$score$boot <- as.numeric(obj_subset$data$score$boot)
max_boot <- obj_subset$data$score %>%
group_by(taxon_id) %>%
summarise(max = max(boot))
max_boot <- setNames(max_boot$max, max_boot$taxon_id)
obj_subset <- filter_taxa(obj_subset, max_boot[taxon_ids] >= min_bootstrap | taxon_ranks %in% c("ASV", "Reference"), reassign_obs = c(abund = TRUE, score = FALSE))
obj_subset$data$asv_prop <- calc_obs_props(obj_subset, 'abund', cols = metadata$sample_name)
#> Calculating proportions from counts for 162 columns for 2697 observations.
obj_subset$data$tax_abund <- calc_taxon_abund(obj_subset, 'abund', cols = metadata$sample_name)
#> Summing per-taxon counts from 162 columns for 1135 taxa
obj_subset$data$tax_prop <- calc_taxon_abund(obj_subset, 'asv_prop', cols = metadata$sample_name)
#> Summing per-taxon counts from 162 columns for 1135 taxa
obj_subset$data$tax_data <- calc_n_samples(obj_subset, 'tax_prop', cols = metadata$sample_name[metadata$sample_type == 'Sample'])
#> Calculating number of samples with a value greater than 0 for 162 columns for 1135 observations
obj_subset$data$tax_data$mean_prop <- rowMeans(obj_subset$data$tax_prop[, metadata$sample_name])
# Calculate the relative standard deviation for each taxon as a measure of how consistently it was found.
rsd <- function(x, na.rm = FALSE) {sd(x, na.rm = na.rm) / mean(x, na.rm = na.rm)}
obj_subset$data$tax_data$rel_stand_dev <- map_dbl(1:nrow(obj_subset$data$tax_abund), function(i) {
rsd(unlist(obj_subset$data$tax_abund[i, metadata$sample_name]), na.rm = TRUE)
})
obj_subset
#> <Taxmap>
#> 1135 taxa: aab. Eukaryota ... cim. NA
#> 1135 edges: NA->aab, aab->aac, aab->aad ... cik->cil, cil->cim
#> 6 data sets:
#> abund:
#> # A tibble: 2,697 × 179
#> taxon_id sequence dada2_tax is_inoculum inoculum S1 S10
#> <chr> <chr> <chr> <lgl> <chr> <int> <int>
#> 1 bfz AAAAAGTCG… Eukaryot… FALSE <NA> 0 0
#> 2 bga AAAAAGTCG… Eukaryot… FALSE <NA> 0 0
#> 3 bgb AAAAAGTCG… Eukaryot… FALSE <NA> 0 0
#> # ℹ 2,694 more rows
#> # ℹ 172 more variables: S100 <int>, S101 <int>, S102 <int>,
#> # S103 <int>, S104 <int>, S105 <int>, S106 <int>, S107 <int>,
#> # S108 <int>, S109 <int>, …
#> score:
#> # A tibble: 22,066 × 4
#> taxon_id sequence boot rank
#> <chr> <chr> <dbl> <chr>
#> 1 aab AAAAAGTCGTAACAAGGTAACCGTAGGTGAACCTGCGGTT… 100 Doma…
#> 2 aac AAAAAGTCGTAACAAGGTAACCGTAGGTGAACCTGCGGTT… 100 King…
#> 3 aae AAAAAGTCGTAACAAGGTAACCGTAGGTGAACCTGCGGTT… 100 Phyl…
#> # ℹ 22,063 more rows
#> tax_abund:
#> # A tibble: 1,135 × 163
#> taxon_id S1 S10 S100 S101 S102 S103 S104 S105 S106
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 aab 56792 35094 50052 42596 85519 70268 52090 54075 51693
#> 2 aac 27698 12210 11111 13034 18437 19077 10116 11106 12154
#> 3 aad 29094 22884 38941 29562 67082 51191 41974 42969 39539
#> # ℹ 1,132 more rows
#> # ℹ 153 more variables: S107 <dbl>, S108 <dbl>, S109 <dbl>,
#> # S11 <dbl>, S110 <dbl>, S111 <dbl>, S112 <dbl>, S113 <dbl>,
#> # S114 <dbl>, S115 <dbl>, …
#> tax_prop:
#> # A tibble: 1,135 × 163
#> taxon_id S1 S10 S100 S101 S102 S103 S104 S105 S106
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 aab 1 1 1 1 1 1 1 1 1
#> 2 aac 0.488 0.348 0.222 0.306 0.216 0.271 0.194 0.205 0.235
#> 3 aad 0.512 0.652 0.778 0.694 0.784 0.729 0.806 0.795 0.765
#> # ℹ 1,132 more rows
#> # ℹ 153 more variables: S107 <dbl>, S108 <dbl>, S109 <dbl>,
#> # S11 <dbl>, S110 <dbl>, S111 <dbl>, S112 <dbl>, S113 <dbl>,
#> # S114 <dbl>, S115 <dbl>, …
#> asv_prop:
#> # A tibble: 2,697 × 163
#> taxon_id S1 S10 S100 S101 S102 S103 S104 S105 S106
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 bfz 0 0 0 0 0 0 0 0 0
#> 2 bga 0 0 0 0 0 0 0 0 0
#> 3 bgb 0 0 0 0 0 0 0 0 0
#> # ℹ 2,694 more rows
#> # ℹ 153 more variables: S107 <dbl>, S108 <dbl>, S109 <dbl>,
#> # S11 <dbl>, S110 <dbl>, S111 <dbl>, S112 <dbl>, S113 <dbl>,
#> # S114 <dbl>, S115 <dbl>, …
#> tax_data:
#> # A tibble: 1,135 × 4
#> taxon_id n_samples mean_prop rel_stand_dev
#> <chr> <int> <dbl> <dbl>
#> 1 aab 162 1 0.444
#> 2 aac 162 0.416 0.652
#> 3 aad 162 0.584 0.711
#> # ℹ 1,132 more rows
#> 0 functions:
set.seed(1000)
obj_subset %>%
filter_taxa(! is_stem) %>%
filter_taxa(n_samples >= 10, supertaxa = TRUE, reassign_obs = FALSE) %>%
filter_taxa(! grepl(x = taxon_names, "_sp$"), reassign_obs = FALSE) %>%
filter_taxa(! grepl(x = taxon_names, "incertae_sedis", ignore.case = TRUE), reassign_obs = FALSE) %>%
filter_taxa(! grepl(x = taxon_names, "NA", ignore.case = TRUE), reassign_obs = FALSE) %>%
remove_redundant_names() %>%
heat_tree(node_size = mean_prop,
edge_size = n_samples,
node_color = ifelse(is.na(rel_stand_dev), 0, rel_stand_dev),
#node_color_range = c("#aaaaaa", "#8da0cb", "#66c2a5", "#a6d854", "#fc8d62", "red"),
node_label = taxon_names,
node_size_range = c(0.008, 0.025),
node_label_size_range = c(0.012, 0.018),
edge_label_size_range = c(0.010, 0.013),
node_size_interval = c(0, 1),
edge_size_range = c(0.001, 0.008),
#layout = "da", initial_layout = "re",
node_color_axis_label = "Relative standard deviation",
node_size_axis_label = "Mean proportion of reads",
edge_size_axis_label = "Number of samples",
node_color_digits = 2,
node_size_digits = 2,
edge_color_digits = 2,
edge_size_digits = 2,
#aspect_ratio = 1.618,
output_file = file.path('standard_workflow/combined_figures', '/heattree_mostabund_taxa.pdf'))
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.3.2 (2023-10-31)
#> os macOS Ventura 13.2.1
#> system aarch64, darwin20
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/Los_Angeles
#> date 2024-11-22
#> pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
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